The space-only model provided a better fit (ρ=0.66ρ=0.66) as compared to the local orientation information (ρ=0.22ρ=0.22), and,

in fact, the combined orientation and spatial information in the full model slightly worsens the prediction (ρ=0.60ρ=0.60). This neuron may thus be largely nonselective to orientation but nevertheless exhibits curvature selectivity at the boundaries of the RF due to spatial inhomogeneity. This highlights to what extent texture- or nonorientation-selective units can exhibit curvature-selective responses at their spatial boundaries. Other cells tuned for high-curvature shapes exhibited similar orientation heterogeneity (Figure 6, top row) and had selectivity LBH589 molecular weight for curved shapes typically at the RF boundary (see examples in Figure S3). To test the predictive power of the model, we computed a null distribution of the correlation coefficients

by repeatedly shuffling the fine-scale orientation maps and then generating response patterns from these shuffled maps (Figure S5A; see Experimental Procedures). This shuffling procedure perturbed the relative spatial structure of the fine-scale CX-5461 price map within a coarse grid location. It thus serves as a comparison against which to test whether contour preferences at a given location depend on the spatial arrangement of the local orientation map. Using this procedure, we calculated whether any of the model correlations (across all spatially significant locations) were significantly different from chance (p = 0.05) after correcting for multiple comparisons. The spatial locations where the model correlations are significant are demarcated with “x” for our example neurons (Figure 7A, lower left panels). Across the population, 80% of neurons showed a significant prediction (i.e., at least one RF location with significant p value; on average

40% of the RF locations had significant p values). The linear pooling model accounts for a substantial fraction of the response variance (see Experimental Procedures) across neurons with varied shape preferences. Figure 7B shows a scatterplot of the mean explained variance (averaged across RF locations) ADAMTS5 for the full model versus average shape preference. The marginal distribution of the mean explained variance has a median value of 0.25. Examining the histogram of explained variance for the full and reduced models (Figure 7C), we see that the orientation-only model plays a dominant role for the straight/low-curvature categories (linear Pearson correlation, r = −0.4, p < 0.001). Note that the local orientation significantly improved fits for medium-curvature neurons (p < 0.001), though not for high-curvature neurons. Thus, for medium curvature, local orientation plays a significant role. Meanwhile, the space-only model plays a key role across all shape categories (r = 0.09, p = 0.02). In general, the full model is the best predictor across the population.